I have problems when I use a foreach loop (using %dopar%
) which invokes a self-defined function. There is not really a problem when I work with Linux, but when I use Windows the self-defined function cannot be found. It is hard to explain the problem in words, so I composed a small example to show it. Assume I have a collection of three simple functions, where FUN2
(using %do%
) and FUN3
(using %dopar%
) invoke the first one (FUN
):
FUN <- function(x,y,z) { x + y + z }
FUN2 <- function(a, b) {
foreach(i=1:3) %do% FUN(i, a, b)
}
FUN3 <- function(a, b) {
foreach(i=1:3) %dopar% FUN(i, a, b)
}
The functions are stored in a script called foreach_testfunctions.R
. In another script (foreach.test
) I source these functions, use library(doParallel)
and try to use the functions. First I do it with Linux and all works fine:
source("foreach_testfunctions.R")
a <- 2
b <- 3
library(doParallel)
registerDoParallel()
foreach(i=1:3) %do% FUN(i, a, b) ## works fine
FUN2(a, b) ## works fine
foreach(i=1:3) %dopar% FUN(i, a, b) ## works fine
FUN3(a, b) ## works fine
Then I do it in Windows:
source("foreach_testfunctions.R")
a <- 2
b <- 3
library(doParallel)
cl <- makeCluster(3)
registerDoParallel(cl)
foreach(i=1:3) %do% FUN(i, a, b) ## works fine
FUN2(a, b) ## works fine
foreach(i=1:3) %dopar% FUN(i, a, b) ## works fine
FUN3(a, b) ## does not work
Error in FUN(i, a, b) : task 1 failed - "Could not find function "FUN""
Conclusion: (1) No problems with %do%
. (2) Problems with %dopar%
when using Windows. I tried inserting the line clusterExport(cl, varlist=c("FUN", "a", "b"), env=environment())
before the line that invokes FUN3
to make sure that the function FUN
and the variables a and b are found in the proper environment, but the error remains.
My questions: Why does Windows behave different than Linux although the code is identical (apart from the different registerDoParallel
syntax)? How can I make sure that Windows does find function FUN
when invoked via function FUN3
?
They behave differently because registerDoParallel
registers an mclapply
backend on Linux, while it registers a clusterApplyLB
backend on Windows. When using an mclapply
backend, there are essentially no data exporting issues, so it works on Linux. But with clusterApplyLB
, you can run into problems if foreach
doesn't auto-export the functions and data that are needed.
You can solve this problem by modifying FUN3
to export FUN
via the .export
option:
FUN3 <- function(a, b) {
foreach(i=1:3, .export='FUN') %dopar% FUN(i, a, b)
}
This solution works on both Linux and Windows, since .export
is ignored by the mclapply
backend.
As pointed out by Hong Ooi, you have an error in your use of clusterExport
, but I wouldn't use clusterExport
to solve the problem since it is backend specific.